Apparatus and methods for tracking progression of measured phenomena

ABSTRACT

An apparatus and method for providing a performance data output for a user is provided. Apparatus may include a computing device including a processor, which may receive a user datum and generate an interface query data structure including at least a query including an input field based on the user datum. The interface query data structure may configure a remote display device to display the input field to the user, receive at least a user-input datum into the input field, retrieve data describing attributes of the user from a database communicatively connected with the processor, and refine the interface query data structure based on data describing attributes of the user from the database. The processor may use a machine learning model including a classifier to correlate the user datum to the interface query data structure and data multipliers into a list and accordingly generate a strategy data.

FIELD OF THE INVENTION

The present invention generally relates to the field of strategiccoaching for entrepreneurs. In particular, the present invention isdirected to an apparatus and methods for data processing relating toproviding a personal performance data output for improving a confidencelevel of a user.

BACKGROUND

It can be difficult to track progress of a measured phenomenon toward atarget. Prior programmatic attempts to resolve this issue have sufferedfrom inadequate user-provided data intake and processing capabilities.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for generating a performance data outputindicative of progression of a measured phenomenon toward matching atarget is provided. The apparatus includes at least a processor. Amemory is communicatively connected to the processor and containsinstructions configuring the at least a processor to receive a userdatum and generate an interface query data structure including at leasta query including an input field based on the user datum. The interfacequery data structure configures a remote display device to display theinput field to the user, receive at least a first user-input datum intothe input field, retrieve data describing attributes of the user from adatabase communicatively connected with the processor, and refine theinterface query data structure based on data describing attributes ofthe user from the database. In addition, the memory configures theprocessor to generate multiple data multipliers based on the firstuser-input datum. Each data multiplier includes multiple data valuesincluding relatively higher data values describing data indicative ofprogress of the user toward matching a target. At least some datamultipliers are generated and scored using a machine learning modelincluding a classifier configured to correlate the user datum and thefirst user-input datum to data describing the target into an orderedlist based on score. Further, the memory configures the processor togenerate a strategy data for the user based on the first user-inputdatum, relatively higher data values, and the ordered list. The strategydata is generated in response to a data multiplier instruction definedas multiplying at least the first user-input datum by relatively higherdata values and displaying feedback to the user according to the orderedlist. The interface query data structure configures the remote displaydevice to display at least the strategy data and receive a seconduser-input datum through the remote display device corresponding to atleast some aspects of the strategy data. The second user-input datumdemonstrates either a similarity or a dissimilarity to the firstuser-input datum. The strategy data is configured to be iterativelyrecalculated based on the second user-input datum by at least themachine-learning model. The remote display device displays theperformance data output to the user.

In another aspect, a method for tracking progress of measured phenomenais provided. The method includes receiving, by a computing device, auser datum, and generating, by the computing device, an interface querydata structure including at least a query including an input field basedon the user datum. The interface query data structure configures aremote display device to display the input field to the user, receive atleast a first user-input datum into the input field, retrieve datadescribing attributes of the user from a database communicativelyconnected with the processor, and refine the interface query datastructure based on data describing attributes of the user from thedatabase. The method includes generating, by the computing device,multiple data multipliers based on the first user-input datum. Each datamultiplier includes multiple data values including relatively higherdata values describing data indicative of progress of the user towardmatching a target. At least some data multipliers are generated andscored using a machine learning model including a classifier configuredto correlate the user datum and the first user-input datum to datadescribing the target into an ordered list based on score. The methodincludes generating, by the computing device, a strategy data for theuser based on the first user-input datum, relatively higher data values,and the ordered list. The strategy data is generated in response to adata multiplier instruction defined as multiplying at least the firstuser-input datum by relatively higher data values and displayingfeedback to the user according to the ordered list. The interface querydata structure configures the remote display device to display at leastthe strategy data, receive a second user-input datum through the remotedisplay device corresponding to at least some aspects of the strategydata, the second user-input datum demonstrating either a similarity or adissimilarity to the first user-input datum. The strategy data isconfigured to be iteratively recalculated based on the second user-inputdatum by at least the machine-learning model. More particularly, if thesecond user-input datum demonstrates a dissimilarity to the firstuser-input datum, the machine learning model may iteratively recalculatestrategy data reflective of the dissimilarity such that strategy dataincludes data describing relatively more of the second user-input datumthan the first user-input datum. In addition, the interface query datastructure configures the remote display device to display theperformance data output to the user.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1A is a block diagram of an embodiment of an apparatus for trackingprogress of measured phenomena;

FIGS. 1B-1C are diagrammatic representations of multiple exemplaryembodiments of a user input field as displayed by a display device ofthe apparatus of FIG. 1A;

FIG. 2 is a diagrammatic representation of a query database;

FIGS. 3A-3D are diagrammatic representations of multiple exemplaryembodiments of output generated by an interface query data structure;

FIG. 4 is a block diagram of exemplary machine-learning processes;

FIG. 5 is a graph illustrating an exemplary relationship between fuzzysets;

FIG. 6 is a flow diagram of an exemplary method for providing a personalperformance data output;

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to anapparatus and methods for tracking progress of measured phenomena towarda target. For convenience of understanding, application of suchapparatus and methods to data processing relating to providing apersonal performance data output for improving a confidence level of auser are provided, but it is to be understood that such exemplars areintended as non-limiting illustrations of a more generalized technicalprocess. Described processes are executed by a computing deviceincluding a processor. Monitoring user performance over a specifiedduration can be advantageous to a user throughout a performanceimprovement or enhancement process. For example, and without limitation,extracting information relevant to a user's particular goals may allowthe user to receive strategic guidance tailored to their goals on anon-going basis and provide for periodic self-evaluation. Generation ofsuch guidance for the user may be based on user responses to interfacequery data structures (e.g., that may appear to the user in the form ofone or more text-based or other digital media-based surveys,questionnaires, lists of questions, examinations, descriptions, etc.)including questions generated from multiple distinct categories,including “morale,” “momentum,” “motivation,” and one or moremultipliers, which increase the proportionate weight attributed to anyone selected category relative to the other remaining categories. Forexample, in the morale category, questions may be tailored to thehistory of the user to assess trends in a user's confidence over aspecified duration (e.g., the past quarter). Morale category-basedquestions may include asking a user to evaluate their proudest pastachievements. The user may respond to such a request by identifying apast achievement, such as “hiking a strenuous mountain trail over tenmiles in length” and subsequently select between various additionalforms of questioning, such as selecting that the user “completed theactivity with relative ease,” or “faced significant difficulty requiringabove-expected rest periods or additional food” to further input how theuser performed in their identified past achievement. The momentumcategory may be tailored to assessing the current state of the user'sconfidence level, such as how likely and/or frequently the user is toagain repeat a particular activity leading to a desired achievement inthe future. The motivation category may be tailored to assessing whatthe user sees for themselves regarding their confidence level, such as atype of self-reflection. For example, data describing the user'smotivation may be input by the user into the computing device forsubsequent assessment in view of the other categories. Example types ofuser-provided motivational input information can include data describingthe user attaining one or more enumerated achievement goals, such as“losing up to fifteen (15) pounds of body fat over the course of severalmonths” or “identifying and eliminating undesirable repetitive behaviorsassociated with obsessive-compulsive disorder (OCD)”.

Aspects of the present disclosure can be used to generate a queryincluding an interface query data structure. An “interface query datastructure,” as used in this disclosure, is an example of data structureused to “query,” such as by digitally requesting, for data results froma database and/or for action on the data. “Data structure,” in the fieldof computer science, is a data organization, management, and storageformat that is usually chosen for efficient access to data. Moreparticularly, a “data structure” is a collection of data values, therelationships among them, and the functions or operations that can beapplied to the data. Data structures also provide a means to managerelatively large amounts of data efficiently for uses such as largedatabases and internet indexing services. Generally, efficient datastructures are essential to designing efficient algorithms. Some formaldesign methods and programming languages emphasize data structures,rather than algorithms, as an essential organizing factor in softwaredesign. In addition, data structures can be used to organize the storageand retrieval of information stored in, for example, both main memoryand secondary memory. Therefore, “interface query data structure,” asused herein, refers to, for example, a data organization format used todigitally request a data result or action on the data. In addition, the“interface query data structure” can be displayed on a display device,such as a digital peripheral, smartphone, or other similar device, etc.The interface query data structure may be generated based on received“user data,” defined as including historical data of the user.Historical data may include attributes and facts about a user alreadyknown. For example, historical data may include data describingpersonality traits, work history, relationship history, educationhistory, mental history, and/or the like. In some embodiments, interfacequery data structure questions may be generated by a machine-learningmodel. As a non-limiting example, the machine-learning model may receiveuser data and output interface query data structure questions.User-provided responses to the interface query data structure questionsmay include textual or visual responses to each “categorical question”,such responses referred to herein as “interface query data structuredata”. As used in this disclosure, a “categorical question” is a type oftext-based question or other digital media-based question within theinterface query data structure questions that related to a particularenumerated category, such as “morale” or “momentum,” etc. Thecategorical question may be at least partially stored as data describingaspects of that category locally on the computing device performing thedescribed processes and/or remotely on a server communicativelyconnected with the computing device. For example, in one or moreembodiments, a categorical question relating to “morale” may inquireinto a user's morale during a strenuous physical activity, such aspreparing for an outdoor (trail) running race and suffering asignificant setback, such as one or more of bone stress injuries (e.g.,shin splints), iliotibial band syndrome (IT band), and/or Achillestendon injuries. More particularly, such a “categorical question” may beposed as: “Please describe your morale after suffering from bone stressinjuries and being required to rest and perform light physical therapyfor 3 consecutive months. The user may provide a text-based response orupload photos corresponding to a respective question to a computingdevice running the described processes. For example, the user mayrespond to this categorical question by inputting a text response of: “Ifelt extremely disheartened and depressed and no longer wanted tocontinue any form of a physical training regimen whatsoever” to indicatean extreme morale depletion. Alternatively, the user may input the textresponse of: “The injury forced me to reflect on running trails usingproper form and hill technique by controlling upward and downwardbouncing movements, also referred to as a runner's ‘gait’.” Thedescribed processes may accordingly use text-recognition methods toparse through the user-input response to this categorical question tosubsequently either suggest completion of additional related categoricalquestions to further ascertain the nature of the user's end objective orgoal, or to develop and present a personal performance improvement plan.In addition, alternative forms of presentation are possible, such asthat the categorical question may be posed using a combination of text,video, audio, and other digital media content delivery forms and theuser's response may be submitted in a similar or dissimilar form. Thatis, a video delivered categorical question may be responded to usingtext, or vice-versa, or the like. Text-recognition, speech-recognitionand/or other applicable data processing techniques may be used by thedescribed processes for data processing needs. For example, in one ormore embodiments, for imagery entered to the computing device, theprocessor may use machine-learning processes, such as optical characterrecognition to assess data received.

Aspects of the present disclosure can also be used to generatemultipliers, which may include data describing the next three (3) ormore achievements of the user. In addition, the multipliers may bedirected to improve, for example, one or more of a pride, confidenceand/or excitement of the user by assisting the described processes inproviding tailored guidance to the user. For example, if the describeddata processes receive user input including data relating tomountaineering in the form of, for example, textual entries or digitalimagery, the multipliers may effectively “multiply,” or increase thedigital magnitude or emphasis on, mountaineering relative to other userdata relating to other activities, such as scuba diving or surfing. Insome embodiments, multipliers may be generated using a machine-learningmodel, which may generate multipliers using the user data and theinterface query data structures. Furthermore, the processor may scorethe multipliers using the user data and the interface query datastructures and present the multipliers in an ordered list based onscore. This is so, at least in part, because the described processes maygenerate at least a strategy for the user to reach their identifiedfuture achievement goals based on the interface query data structuresand multipliers.

The strategy may include a task or step tailored to the user for them toreach their identified future achievement. For example, for some users,the achievement may be related to addressing mental health disorders,such as obsessive-compulsive disorder. Accordingly, the describedprocesses may generate a strategy, or in some instances multiplestrategies, which may include data output in the form of the followingexample textual phrases: (1) “focusing on progress, not perfection;” (2)“delegation of routine activities;” and, (3) “daily meditation.” Inaddition, the generated strategy may include a task or step tailored tohelp user reach a particular multiplier. Returning to the earlierexample of mountaineering, should the user provide user data and/orresponses to surveys (e.g., described by interface query datastructures) reflecting interest in mountaineering over other activities,then described processes may generate a strategy suggesting a trainingprogram to prepare for certain types of terrain and/or climates, furthermultiplying interest in mountaineering over other categories.Eventually, in some instances, as user interests may change over time,data describing certain interests may be diminished relative to otherinterests or eliminated altogether in consideration by the describedprocesses to optimally provide ongoing guidance and feedback tailored tothe user's current needs and interests. In some embodiments, theprocessor may use a machine learning model, such as a classifier, togenerate one or more strategies. For example, elements of interfacequery data structures and multipliers may be classified to a pluralityof strategies using a classifier.

In some embodiments, a Graphical User Interface (GUI) provided by adisplay device is communicatively connected to the processor.Accordingly, aspects of the present disclosure allow for the displaydevice to display one or more strategies generated by the describedprocesses. In addition, the display device may receive user input foreach strategy displayed. That is, the user may provide user input to thedisplay device that may include the user ranking their progress with arespective strategy. For example, the described processes may requestthe user to indicate on a scale from “1” to “5” how they believe areprogressing with the provided strategy or strategies, where “1” denotes“rarely” or “poorly” and 5 “always” or “perfectly.” Furthermore, theuser input may include a description of specific actions a user istaking in response to a strategy, such as how, when, where, and afrequency of a respective action. Accordingly, processor may receive theuser input through the display device to track the progress of the user.In some embodiments, the processor may use an inference engine ormachine learning to assess the progress of the user and use related datato amend, generate and output new strategies based on user progress. Theprocessor may assess how often/long a user stays on track, what specificactions have the most positive impact, and/or the like. In addition, orthe alternative, the processor may assess and recalibrate the strategieson a monthly, quarterly, or yearly basis to output current strategies tothe user. Exemplary embodiments illustrating aspects of the presentdisclosure are described below in the context of several specificexamples.

Referring now to FIG. 1A, an exemplary embodiment of apparatus 100A(also referred to in this disclosure as a “performance coachingapparatus” or “apparatus”) for processing data relating to providing apersonal performance data output for improving a confidence level of auser is illustrated. In one or more embodiments, apparatus 100A includescomputing device 104A, which may include without limitation amicrocontroller, microprocessor (also referred to in this disclosure asa “processor”), digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device 104A may includea computer system with one or more processors (e.g., CPUs), a graphicsprocessing unit (GPU), or any combination thereof. Computing device 104Amay include a memory component, such as memory component 140A, which mayinclude a memory, such as a main memory and/or a static memory, asdiscussed further in this disclosure below. Computing device 104A mayinclude a display component, as discussed further below in thedisclosure. In one or more embodiments, computing device 104A mayinclude, be included in, and/or communicate with a mobile device such asa mobile telephone or smartphone. Computing device 104A may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Computingdevice 104A may interface or communicate with one or more additionaldevices, as described below in further detail, via a network interfacedevice. Network interface device may be utilized for connectingcomputing device 104A to one or more of a variety of networks, and oneor more devices. Examples of a network interface device include, but arenot limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, any combination thereof, and thelike. Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, acampus, or other relatively small geographic space), a telephonenetwork, a data network associated with a telephone/voice provider(e.g., a mobile communications provider data and/or voice network), adirect connection between two computing devices, and any combinationsthereof. A network may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software etc.) may be communicated to and/or from acomputer and/or a computing device. Computing device 104A may includebut is not limited to, for example, a computing device or cluster ofcomputing devices in a first location and a second computing device orcluster of computing devices in a second location. Computing device 104Amay include one or more computing devices dedicated to data storage,security, distribution of traffic for load balancing, and the like.Computing device 104A may distribute one or more computing tasks, asdescribed below, across a plurality of computing devices of computingdevice 104A, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. Computing device 104A may be implemented using a“shared nothing” architecture in which data is cached at the worker, inan embodiment, this may enable scalability of apparatus 100A and/orcomputing device 104A.

With continued reference to FIG. 1A, computing device 104A may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 104A may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Computing device 104A may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1A, computing device 104A is configuredto receive at least an element of user datum 108A. For the purpose ofthis disclosure, “user datum” references an element, datum, or elementsof data describing historical data of a user (e.g., attributes and factsabout a user that are already known including, for example, personalitytraits, work history, relationship history, education history, mentalhistory, and/or the like). In some embodiments, user datum 108A may benumerically quantified (e.g., by data describing discrete real integervalues, such as 1, 2, 3 . . . n, where n=a user-defined or priorprogrammed maximum value entry, such as 10, where lower values denoterelatively less significant achievements and higher values denoterelatively more significant achievements). For example, in exampleswhere described processes relate to providing a personal performancedata output for improving a confidence level of a user in academia, userdatum 108A may equal “3” for a user holding only a high-school diploma,a “5” for a baccalaureate degree, and an “8” for a doctoral orprofessional degree.

Alternatively, in other examples where described processes relate toimproving a confidence level of a user in a professional setting, userdatum 108A may equal a “3” for performing slightly beneath (e.g., 20%)an enumerated sales or other output performance target, a “5” forachieving exactly the enumerated sales or other output performancetarget, an “8” for performing slightly above (e.g., 20%) the enumeratedsales or other output performance target, or a “10” for greatlyexceeding (e.g., 50%+) the enumerated sales or other output performancetarget. Other example values are possible along with other exemplaryattributes and facts about a user that are already known and may betailored to a particular situation where performance improvement issought. For example, in addition to the above-described scenariosrelating to academia or business output, user datum 108A may includeperformance history relating to extreme sports (e.g., mountaineering),interpersonal relationships (e.g., romantic relationships, dating, etc.)and/or the like. In one or more alternative embodiments, user datum 108Amay be described by data organized in or represented by lattices, grids,vectors, etc. and may be adjusted or selected as necessary toaccommodate particular user-defined circumstances or any other format orstructure for use as a calculative value that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure.

In one or more embodiments, user datum 108A may be provided to orreceived by computing device 104A using various means. In one or moreembodiments, user datum 108A may be provided to computing device 104A bya user, such as a student, working professional, athlete or hobbyist orother person that is interested in increasing and/or improving theirperformance in a particular area or field over a defined duration, suchas a quarter or six months. A user may manually input user datum 108Ainto computing device using, for example, a graphic user interface (GUI)and/or an input device. For example, and without limitation, a user mayuse a peripheral input device to navigate the graphic user interface andprovide user datum 108A to computing device 104A. Non-limiting exemplaryinput devices include keyboards, joy sticks, light pens, tracker balls,scanners, tablet, microphones, mouses, switches, buttons, sliders,touchscreens, and the like. In other embodiments, user datum 108A may beprovided to computing device 104A by a database over a network from, forexample, a network-based platform. User datum 108A may be stored in adatabase and communicated to computing device 104A upon a retrievalrequest form a user and/or from computing device 104A. In otherembodiments, user datum 108A may be communicated from a third-partyapplication, such as from a third-party application on a third-partyserver, using a network. For example, user datum 108A may be downloadedfrom a hosting website for a particular area, such as a meeting groupfor trail runners, or for a planning group for mountaineeringexpeditions, or for performance improvement relating to increasingbusiness throughput volume and profit margins for any type of business,ranging from smaller start-ups to larger organizations that arefunctioning enterprises. In one or more embodiments, computing device104A may extract user datum 108A from an accumulation of informationprovided by a database. For instance, and without limitation, computingdevice 104A may extract needed information from database 132A regardingimprovement in a particular area sought-after by the user and avoidtaking any information determined to be unnecessary. This may beperformed by computing device 104A using a machine-learning model, whichis described in this disclosure further below.

At a high level, “a machine-learning model” describes a field of inquirydevoted to understanding and building methods that “learn”—that is,methods that leverage data to improve performance on some set of definedtasks. Machine learning algorithms may build a machine-learning modelbased on sample data, known as “training data”, in order to makepredictions or decisions without being explicitly programmed to do so.Such algorithms may function by making data-driven predictions ordecisions by building a mathematical model from input data. These inputdata used to build the machine learning model may be divided in multipledata sets. In one or more embodiments, three data sets may be used indifferent stages of the creation of the machine-learning model:training, validation, and test sets.

Described machine-learning models may be initially fit on a trainingdata set, which is a set of examples used to fit parameters. Here,training data sets may include interface query data structures includingquestions at a relatively higher level of generality relating to auser's sought-after area of performance improvement to initiallyascertain user preferences prior to initiating subsequent more-detailedquestioning relating to milestones associated with favorable progressionof the user towards their achievement objectives. For example, in anexample of mountaineering, training data may include user-providedresponses to basic questioning regarding the user's prior trail hiking,outdoor navigation, and mountain climbing experiences. Interface querydata structure questions used to generate or populate training data mayinclude the following: “Please describe whether or not you are able tocomplete a hike of 7 to 10 miles round trip that is moderately strenuoushaving a numerical rating of 100-150 as defined by the US National ParkService,” or “Please describe whether or not you are able to complete ahike of 7 to 10 miles round trip that has an overall elevation gain of2,200 feet.” User-provided responses may be a “yes” or “no,” or includephrases or sentences provided in text format that the describedprocesses may recognize using text-recognition or another applicabledata processing technique. User-provided responses may be incorporatedinto interface query data structure datum 108A to be later iterativelycorrelated by relative applicability to what is determined to be theuser's ultimate performance achievement objective. That is, responses toquestions that are more aligned with improvements in mountaineering maybe weighted relatively higher by the described processes such that apersonal performance improvement data or plan relating to mountaineeringis ultimately created and displayed by display device 128. Other typesof data sets may also be used by the described processes to determinefit and predictive ability, such as validation data sets and final oneor more test data sets. Validation data sets may be incrementally morefocused toward an identified particular aspect of the user's goals thatemerges as more prominent than others. For example, in mountaineering,the user may be (as determined by iterative responses to interface querydata structure questions) more interested in ice climbing thanbouldering. This pattern may be observed by the described processes(e.g., by machine learning module 124) by correlating user providedresponses to interface query data structure questions with one or moresub-categories within mountaineering, such as those that have emergedfrom or may be extracted from user-provided responses to one or moreiterations of interface query data structure questions. Suitablesub-categories in this example can include, at a minimum, indoorclimbing, sport climbing and bouldering, etc.

In one or more embodiments, database 132A may include inputted orcalculated information and datum related to improvement in a particulararea sought-after by the user. A datum history may be stored in adatabase 132A. Datum history may include real-time and/or previousinputted interface query data structure 112A and user datum 108A. In oneor more embodiments, database 132A may include real-time or previouslydetermined record recommendations and/or previously provided interactionpreparations. Computing device 104A may be communicatively connectedwith database 132A. For example, and without limitation, in some cases,database 132A may be local to computing device 104A. In another example,and without limitation, database 132A may be remote to computing device104A and communicative with computing device 104A by way of one or morenetworks. A network may include, but is not limited to, a cloud network,a mesh network, and the like. By way of example, a “cloud-based” systemcan refer to a system which includes software and/or data which isstored, managed, and/or processed on a network of remote servers hostedin the “cloud,” e.g., via the Internet, rather than on local severs orpersonal computers. A “mesh network” as used in this disclosure is alocal network topology in which computing device 104A connects directly,dynamically, and non-hierarchically to as many other computing devicesas possible. A “network topology” as used in this disclosure is anarrangement of elements of a communication network. Network may use animmutable sequential listing to securely store database 132A. An“immutable sequential listing,” as used in this disclosure, is a datastructure that places data entries in a fixed sequential arrangement,such as a temporal sequence of entries and/or blocks thereof, where thesequential arrangement, once established, cannot be altered orreordered. An immutable sequential listing may be, include and/orimplement an immutable ledger, where data entries that have been postedto the immutable sequential listing cannot be altered.

Database 132A may include keywords. As used in this disclosure, a“keyword” is an element of word or syntax used to identify and/or matchelements to each other. For example, without limitation, a keyword maybe “mountaineer” in the instance that a user is looking to prepare for astrenuous expedition in a challenging geographic region, such as theHimalayas or the Karakoram range. In another non-limiting example, akeyword may be “surfer” in an example where the user is seeking toprepare for surfing in, for example, Malibu or various locations inHawaii. Database 132A may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art, upon reviewing the entirety of thisdisclosure, would recognize as suitable upon review of the entirety ofthis disclosure.

With continued reference to FIG. 1A, computing device 104A is furtherconfigured to receive user datum 108A, as previously mentioned. For thepurposes of this disclosure, “user datum” is historical data of user.Historical data may include attributes and facts about a user alreadyknown. For example, personality traits, work history, relationshiphistory, education history, mental history, and the like. User datum108A may be audio and/or visual information related to the user'spersonal information, attributes, and/or credentials. For example, userdatum 108A may be a video, digital photo, audio file, text, and thelike. User datum 108A may include a user's prior record, such as a draftresume, personal address, social security number, phone number,employment history, experience level, education, certification, acquiredskills, geographical location, expected compensation, career performanceacknowledgements (e.g., awards, honors, distinguishments), photograph ofuser, sample work product, and/or the like. User datum 108A may bereceived by computing device 104A by the same or similar means describedabove. For example, and without limitation, user datum 108A may beprovided by a user directly, database, third-party application, remotedevice, immutable sequential listing, and/or the like. In non-limitingembodiments, user datum 108A may be provided as independent orunorganized facts, such as answers to prompted questions provided bycomputing device 104A and/or as dependent or organized facts, such as apreviously prepared record that the user made in advance. In one or moreembodiments, after receiving interface query data structure 112A and/oruser datum 108A, computing device 104A may determine interface querydata structure recommendation 116A as a function of interface query datastructure 112A and/or user datum 108A. For instance, and withoutlimitation, interface query data structure recommendation 116A mayinclude a suggested alteration and/or change, such as an addition ordeletion of a portion of previously prepared interface query datastructure. In another instance, and without limitation, interface querydata structure recommendation 116A may include an automatedly generatedrecord created by computing device 104A. In another instance, andwithout limitation, interface query data structure recommendation 116Amay include instructions and/or directions to user describing a processfor creating a new customized interface query data structure, such as acustomized interface query data structure for a particular area or fieldsought for performance improvement. In one or more embodiments, languageprocessing, such as by processor 144A, may be used to identifyuser-related data from available resources (e.g., publicly accessiblemailing addresses, educational and/or job histories, etc.) to replacethe user-related data with user-specific data for the user, such as userdatum 108A and/or interface query data structure 112A. In addition,interface query data structure 112A may be generated by processor 144Abased on user datum 108A and further refined by processor 144A in one ormore iterations of the presently disclosed processes. That is, processor144A may generate interface query data structure 112A based onuser-provided responses to interface query data structure recommendation116A, which may include questions tailored to categories such as morale,momentum, motivation, and multipliers. For this disclosure, “interfacequery data structure datum” references an element, datum, or elements ofdata based on user-provided responses to interface query data structurerecommendation 116A (e.g., indications of interest in pursuing aparticular hobby, interest, pastime, occupation, achievement, goaland/or the like). In some embodiments, interface query data structure112A may be numerically quantified (e.g., by data describing discretereal integer values, such as 1, 2, 3 . . . n, where n=a user-defined orprior programmed maximum value entry, such as 10, where lower valuesdenote relatively less significant achievements and higher values denoterelatively more significant achievements).

User-provided responses to interface query data structures generated byinterface query data structure recommendation 116A may be used byprocessor 144A to generate interface query data structure 112A. Forexample, in the context of mountaineering, interface query datastructure recommendation 116A may initially request the user to input(e.g., by user datum 108A) a range of climbing or bouldering ratingsindicative of the user's current skill level (e.g., 5.13 to 5.15indicative of the “Very Difficult” sub-category of Class 5 routes).Based on user-provided responses to interface query data structurerecommendation 116A, processor 144A may initially generate or defineinterface query data structure 112A as including the user's currentskill level as identified. Next, processor 144A may update or reviseinterface query data structure recommendation 116A to request the userto identify if their intended skill development area encompasses iceclimbing, often understood to be a more technical or dangerous form ofclimbing. Upon receiving an affirmative response from the user,processor 144A may then even further update interface query datastructure recommendation 116A to present additional requests forinformation or clarifications pertaining to ice climbing technicalspecifics, including (but not limited to) “how steep the ice is,” “theice quality,” “the availability of protection,” “the technicality ofmovements,” “the length of the route,” and/or “availability of restspots.” Then, upon receiving user-provided responses to these specificquestions, processor 144A may iteratively update interface query datastructure datum. In this way, interface query data structure 112A may begenerated based on one or more of textual or visual responses (e.g.,provided by user datum 108A and/or responses to interface query datastructure recommendation 116A) to each categorical question (e.g., ofinterface query data structure recommendation 116A) provided by theuser. In one or more embodiments, computing device may present interfacequery data structure recommendation 116A to a user, such as suggest anaddition or deletion of a word, phrase, image, or part of an image(collectively referred to in this disclosure as an “object”) from apreviously prepared interface query data structure, or may automatedlyexecute record recommendation 116A, such as an automated addition ordeletion of an object from a previously prepared interface query datastructure automatically generates an iteratively customizable interfacequery data structure by computing device 104A. In addition, iterationsmay be either displayed or not displayed to the user and limited, suchas by the user, based on a total overall refinement preference forinterface query data structure 112A. Interface query data structurerecommendation 116A may be presented using, for example and withoutlimitations, using a display of apparatus 100A, as discussed further inthis disclosure below.

In one or more embodiments, interface query data structurerecommendation 116A may include suggested recommendations for a digitalmedia (e.g., digital videos, digital photos, etc.) interface query datastructure or questionnaire. For instance, and without limitation,computing device 104A may be configured to intake and record responsesto the digital media interface query data structure or questionnaire. Aninitial pass may be used by computing device 104A to sort elements ofdigital media interface query data structures into categories (e.g.,morale, momentum, motivation, and multipliers), and a subsequent passmay involve detailed further iterative evaluation of additional digitalmedia-based questioning including selection of subsequent interfacequery data structure questions based on prior interface query datastructure responses. For example, the initial pass may includeclassifying digital media interface query data structures (e.g., storedin database 132A) based on an image component, an audio component, userdatum, or at least identifying user indica. For example, identifyingindica could include personal information of user such as a name of useror subject, account number, social security number, telephone number,address, and the like, or usage of key terms, words or phrasesrepresentative of an area or field in which the user seeks to improvetheir performance (e.g., mountaineering or surfing). Processor 144A maythen search within database 132A to retrieve and output digital mediainterface query data structures related to and/or further refining thearea or field in which the user seeks to improve their performance. Forexample, in some embodiments, computing device 104A may utilize acandidate classifier, which may include any classifier used throughoutthis disclosure, to run an initial pass over the digital media elementsof digital media resumes, break down and categorizes such elementsbefore comparing it to target digital media resume.

A “classifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm” thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum (e.g., user datum 108A and/orinterface query data structure 112A, as well as elements of dataproduced by described processes) that labels or otherwise identifies aset of data that are clustered together, found to be close under adistance metric, or the like. As used in this disclosure, a “strategyclassifier” is a classifier that classifies users to a target strategyor classifies data describing groupings of related strategies in one ormore hierarchies, each organized to present strategies (e.g., in visual,audial, and/or textual format, etc.) to the user via display device 128Ain, for example, a descending order of relevance to more convenientlyassist the user to attain their desired achievement or goal. In somecases, a strategy classifier may include a trained machine-learningmodel, which is trained using strategy training data. As used in thisdisclosure, “strategy training data” is a training data that correlatesone or more of users and user datum 108A to one or more strategies,groupings of strategies related by common subject matter, andrefinements of strategies responsive to user input.

Referring again to FIG. 1A, computing device 104A may be configured togenerate one or more strategies corresponding to the user progressingtoward their achievement goals by strategy data generation 138A and/oras a function of any training data as discussed in this disclosure andclassifying data describing strategies generated by strategy datageneration 138A. As used in this disclosure, “strategy data” is datadescribing one or more tasks or steps tailored to help the user progresstowards reaching their enumerated achievement goal. Such strategy datamay be generated by computing device 104A performing one or moredescribed processes by, for example, interface query data structurerecommendation 116A, data multiplier generation 120A, machine learningmodule 124A, and data multiplier scoring 136A. In one or moreembodiments, described processes may parse through strategy dataproduced by strategy generation 138A to output, in textual, video,graphic (e.g., charts, tables, etc.) or some other suitable digitalmedia-based format, relating to providing the user a personalizedperformance improvement plan tailored to reaching their enumeratedachievement goals. A classifier may be configured to output at least adatum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Computing device 104A and/or anotherdevice may generate a classifier using a classification algorithm,defined as a process whereby a computing device 104A derives aclassifier from training data. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1A, computing device 104A may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104A may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104A may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1A, computing device 104A may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

Further referring to FIG. 1A, generating k-nearest neighbors algorithmmay generate a first vector output containing a data entry cluster,generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm:

${l = \sqrt{{\sum}_{i = 0}^{n}a_{i}^{2}}},$where a_(i) is am-mute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

Still referring to FIG. 1A, in one or more embodiments, apparatus 100Afor providing a performance data output for a user is provided.Apparatus 100A includes processor 144A in computing device 104A, wherememory component 140A is communicatively connected to processor 144A.Memory component 140A may contain instructions configuring processor144A to receive user datum 108A and correspondingly generate interfacequery data structure 112A including at least a query including an inputfield (e.g., that may be displayed as user input field 130A by displaydevice 128A) based on user datum 108A. In some instances, interfacequery data structure 112A is at least partially based on data describingattributes of the user that are retrieved from database 132A includingcategorical information correlated to a historical range of data, suchas over 3 months, or 6 months, etc. At least some attributes retrievedfrom database 132A describe data relating to motivation of the userdefined as a frequency of completing activities related to the strategydata.

More particularly, interface query data structure 112A may be generatedbased on at least user datum 108A by computer device 104A completing oneor more processes or steps. For example, processor 144A of computingdevice 104A may intake user datum 108A upon, for example, user-providedentry, which may include manually typing text into display device 128Aor clicking on interactive icons presented by display device 128A.Processor 144A may then parse, review, or otherwise process user datum108A to generate a query, and then use the generated query to database132A and specifically retrieve information from database 132A that isparticularly relevant to user datum 108A. For example, should user datum108A include data describing the user's geographical proximity to abeach and their desire to improve hiking performance for traversinghilly trails near beaches, then processor 144A may retrieve relevantinformation from database 132A, such as a hiking map of several trailsin Laguna Beach, California. That relevant information may then be usedto create interface query data structure 112A, which may include asurvey having questions presented in textual and/or graphical (e.g.,digital photos, etc.) format to the user. The user may then provideinput, e.g., a first user-input datum, responsive to prompts presentedby interface query structure 112A, iteratively, such that describedprocesses may more narrowly tailor questioning to specifically help theuser successfully attain their specific performance improvement goals.

In some embodiments, interface query data structure 112A may generate aquery to be displayed in an interface, such as by display device 128Ausing one or more event handlers for receiving user selections, textualentries, links, images, videos, uploads, etc. In addition, or thealternative, in some instances, interface query data structure 112A mayalso be tasked with receiving a user response (e.g., a first user-inputdatum), or one or more separate data structures may be used to storeuser response related information. In some instances, interface querydata structure 112A may be a .PHP or .JSP or similar type of file thatmay direct display, such as by display device 128A, or user-interactionfields on display device 128A displaying interface query data structure112.

Accordingly, interface query data structure 112A may configure displaydevice 128A to display user input field 130A to the user, receive atleast a first user-input datum into user input field 130A, retrieve datadescribing attributes of the user from database 132A communicativelyconnected with the processor, and refine interface query data structure112A based on data describing attributes of the user from database 132A.In some instances, display device 128A may be positioned remotely fromcomputing device 104A and/or display user input field 130A to the userby a Graphical User Interface (GUI) defined as a point of interactionbetween the user and display device 128A. In addition, the GUI maydisplay refinements to the interface query data structure based on datadescribing attributes of the user from database 132A as a second inputfield (e.g., which may also be displayed in user input field 130A bydisplay device 128A). Memory component 140A contains furtherinstructions configuring processor 144A to generate multiple datamultipliers based on the first user-input datum. Each data multipliermay include multiple data values. At least some data multipliers aregenerated and scored (e.g., in order of relevance to user-provided inputregarding the user's achievement objectives) using machine learningmodule 124A (e.g., which may run any machine learning model describedfurther below). Machine learning module 124A may include a classifierthat correlates user datum 108A to the interface query data structure112A and multiple data multipliers into an ordered list based on score.In addition, or the alternative, in one or more embodiments, theclassifier of machine learning model 134A may score or identify at leastan element of at least some data multipliers between a minimum value anda maximum value. In some embodiments, a user-input datum (e.g., a secondor consecutive user-input datum) includes data describing currentpreferences of the user and the classifier of the machine learning modelis configured to correlate the user datum, a first user-input datum, andthe second user-input datum to data describing the target anditeratively generate an ordered list between a minimum value and amaximum value.

Data describing aspects of a user's behavior that more closely matchestheir objectives for personal performance improvement may be presented(and re-presented) to the user by interface query data structure 112Asuch that described processes may develop a personal performance dataoutput that accurately matches the user's overall objectives. Further,in some embodiments, the classifier may perform “data collection” fromthe database 132A, where “data collection” is defined as gathering andmeasuring data related to at least one targeted variable. In addition,the classifier may classify how a user provides input to interface querydata structure 112A to thereby apply multiple data multipliers to inputfound to be more closely related to the user's overall objectives,thereby further reinforcing and increasing weightage attributed to datadescribing concepts relevant to the user's overall objectives.

In some instances, machine learning module 124A may use the classifierand classify data describing the frequency of the user completingactivities associated with the personal performance data output andupdate the personal performance data output accordingly. In someinstances, machine learning model may use the classifier and classifydata describing the frequency of the user completing activitiesassociated with the personal performance data output and update thepersonal performance data output. More particularly, wherein theperformance data output (e.g., graph 316D) may be iteratively updated bya classifier of a machine learning model (e.g., machine learning module124A). The classifier may classify data describing a frequency of theuser completing activities describing progress of the user towardmatching a target to strategy data (e.g., content display area 312A).Memory component 140A may contain further instructions configuringprocessor 144A to generate a strategy data (e.g., by strategy datageneration 138A) based on the first user-input datum and multiple datamultipliers. The strategy data may include a data multiplier instructionthat multiplies at least some data multipliers based on the orderedlist. Interface query data structure 112A may configure display device128A to display the strategy data, receive a second user-input datumthrough the remote display device corresponding to the strategy data,and provide the personal performance data output as a function of the atleast an element of the first user-input datum, the second user-inputdatum and the data multiplier instruction. In addition, in one or moreembodiments, if the second user-input datum demonstrates a dissimilarityto the first user-input datum, the machine learning model iterativelyrecalculates strategy data (e.g., as displayed in content display area312A) reflective of the dissimilarity such that strategy data includesdata describing relatively more of the second user-input datum than thefirst user-input datum. Further, in some instances, the machine learningmodel may be configured to apply an “information momentum multiplier” tothe user datum and/or the interface query data structure, wherein the“information momentum multiplier” is defined by the second user-inputdatum exceeding a pre-defined numerical threshold. In some instances,processor may update the strategy data based on the second user-inputdatum at periodic intervals. In addition, or the alternative, in someembodiments, interface query data 112A structure may configure displaydevice 128A to display the personal performance data output as afunction of a user data change descriptor generated based on the seconduser-input datum. In some instances, the first user-input datum and/orthe second user-input datum may include at least an element of datadescribing a user-responsiveness factor defined as a frequency of theuser in completing activities associated with the personal performancedata output. In addition, machine learning module 124A may performoptical character recognition and process data associated with the firstuser-input datum and/or the second user-input datum.

Referring now to FIGS. 1B-1C, exemplary embodiments of user input field130A as displayed by display device 128A are illustrated. For example,screen 100B and screen 100C may be displayed by display device 128A,which may be a “smart” phone, such as an iPhone, or other electronicperipheral or interactive cell phone, tablet, etc. Screen 100B may be aninitial screen including multiple fields, including identification field104B, entry field 108B, instruction field 112B, and multiple interactiveuser input fields including a first user input field 116B, a second userinput field 120B, a third user input field 124B, a fourth user inputfield 128B, and a fifth user input field 132B. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious ways in which fewer or additional interactive user input fieldsmay be displayed by screen 100B. Identification field 104B may identifydescribed processes performed by processor 144A of computing device 104Aby displaying identifying indicia, such as “Personal Performance DataAssessment and Strategy Development” as shown in FIG. 1B. Entry field108B may show indicia explaining what type of instructions or questionsmay be shown in instruction field 112B. Initially, upon entry field 108Bmay display “Area of Focus Selection” indicative of permitting the userto input their particular area of interest in which personal performanceimprovement is sought. Multiple interactive user input fields (e.g.,which may be an example of user input field 130A) may then pose a query(e.g., a survey of questions as shown in FIG. 1B) to the user forfeedback in the form of user-provided input such that describedprocesses performed by processor 144A may progress to screen 100C, whichis an incremental narrowing progression of questioning relating to moreparticularly identifying user aspirations.

Similar to screen 100B, screen 100C may be an example of a subsequentscreen including multiple fields, including identification field 104C,entry field 108C, instruction field 112C, and multiple interactive userinput fields including a first user input field 116C, a second userinput field 120C, a third user input field 124C, a fourth user inputfield 128C, and a fifth user input field 132C. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious ways in which fewer or additional interactive user input fieldsmay be displayed by screen 100C. Identification field 104C may identifydescribed processes performed by processor 144A of computing device 104Aby displaying identifying indicia, such as “Personal Performance DataAssessment and Strategy Development” as shown in FIG. 1C. Entry field108B may show indicia explaining what type of instructions or questionsmay be shown in instruction field 112B. Initially, upon entry field 108Cmay display “Entry of Specific Interests” indicative of permitting theuser to input their particular area of interest in which personalperformance improvement is sought. Multiple interactive user inputfields (e.g., which may be an example of user input field 130A) may thenpose a query (e.g., a survey of questions as shown in FIG. 1C) to theuser for feedback in the form of user-provided input such that describedprocesses performed by processor 144A may progress to subsequentscreens, each being an incremental narrowing progression of questioningrelating to more particularly identifying user aspirations.

Referring now to FIG. 2 , an exemplary embodiment of query database 200is illustrated. In one or more embodiments, query database 200 may be anexample of database 132 of FIG. 1 . Query database may, as anon-limiting example, organize data stored in the query databaseaccording to one or more database tables. One or more database tablesmay be linked to one another by, for instance, common column values. Forinstance, a common column between two tables of expert database mayinclude an identifier of a query submission, such as a form entry,textual submission, or the like, for instance as defined below; as aresult, a query may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of query data, identifiers of interfacequery data structures relating to obtaining information from the user,times of submission, or the like. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which query data from one or more tables may be linked and/or relatedto query data in one or more other tables. In addition, in one or moreembodiments, computing device 104A may be configured to access andretrieve one or more queries from query database 200. Each query mayinclude data describing one or more interface query data structuresincluding questions requesting information relating to specific detailsfor the user progressing toward their achievement goals.

Still referring to FIG. 2 , one or more database tables in querydatabase 200 may include, as a non-limiting example, morale category204, which may be used to store records indicating interface query datastructures including data describing questions relating to morale of theuser, or the like. Data describing interface query data structuresand/or related interface query data structure questions may be accessedfrom query database 200 to be processed by computing device 104A andoutput by display device 128 in the form of text, digital videos,digital photos and/or the like. Example types of morale questions orinterface query data structures can include one or more of the followingas relating to a particular aspiration, achievement, or goal (e.g.,mountaineering): “please quantify your confidence level from 1-10 afterslipping from an artificial climbing hold, with “1” being devastated to“10” being unperturbed;” “please describe your ideal expeditionaspirations over the next quarter,” “please indicate your climbingmentors,” and/or “please describe your training discipline to prepare toclimb the Karakoram range” and/or the like.

As described here, questions may quantifiable or non-quantifiable.Questions that are non-quantifiable may be recognized by audiovisualspeech recognition (AVSR) processes to recognize verbal (e.g.,dictation) content as described here or other processes for subsequentdata retention, storage, and processing by computing device 104. One ormore tables may also include a momentum category 208, which may storedata describing momentum related questions or interface query datastructures. Example types of momentum questions or interface query datastructures can include one or more of the following as relating to aparticular aspiration, achievement, or goal (e.g., mountaineering):“please quantify your energy level from 1-10 after slipping from anartificial climbing hold, with “1” being fully depleted to “10” beingunperturbed; and/or “please describe your training discipline relatingto strength, force and drive for preparing to climb the Karakoram range”and/or the like. In addition, one or more tables may include motivationcategory 212, which may store data describing motivation relatedquestions or interface query data structures. Example types ofmotivation questions or interface query data structures can include oneor more of the following as relating to a particular aspiration,achievement, or goal (e.g., mountaineering): “please quantify yourenergy level from 1-10 after slipping from an artificial climbing hold,with “1” being fully depleted to “10” being unperturbed; and/or “pleasedescribe your training discipline relating to strength, force and drivefor preparing to climb the Karakoram range” and/or the like. Inaddition, one or more tables may include motivation category 212, whichmay store data describing momentum related questions or interface querydata structures. Example types of morale questions or interface querydata structures can include one or more of the following as relating toa particular aspiration, achievement, or goal (e.g., mountaineering):“please describe the reason or reasons you have for acting or behavingin a particular way relating to mountaineering;” “please describe yourdesire or willingness to progress in mountaineering skill level;” or“please describe the strongest motivational factor out of (1)incentives; (2) fear; (3) power, or (4) social accolades” and/or thelike.

In an embodiment, and still referring to FIGS. 1 and 2 , computingdevice 104A may be configured to access, categorize, and/or sort datadescribing any one or more of morale category 204, momentum category208, and/or motivation category 212 for further manipulation bymultipliers 216, which may be as described earlier with relation to datamultiplier scoring 136. As used in this disclosure, a “data multiplier”is a calculative tool used for measuring how important one type of datais to another type within described processes performed by computingdevice 104A of FIG. 1 . For instance, multiplier 216 may store one ormore multiplicative data values, such as “3×”, “5×” and/or the like,where each multiplicative data value may be accessed during datamultiplier scoring 136A by computing device 104. Multiplier data frommultipliers 216 may thereby proportionately increase weightage orattribution to a particular form of guidance based on, for example, userdatum 108A and/or interface query data structure 112A such that thedescribed processes will output personal performance data relating tothe user's areas of interest.

In one or more embodiments, computing device 104A may generate datamultipliers to multiply data describing user-provided responses tointerface query data structure recommendation 116A to, for example,proportionately increase weight or consideration provided to areas orfields identified by the user as being of particular interest orsignificance. For example, should interface query data structurerecommendation 116A be initially provided at a high-level relating tosports and recreation, such as requesting the user “to indicate whatoutdoor activities they wish to participate in and improve theirperformance in over time,” the user may provide a variety of responses,the majority of which may focus on mountaineering with the balance onother activities, such as surfing, swimming, rowing, snowboarding,skiing and/or the like. Data multiplier generation 120A mayproportionately increase emphasis placed on mountaineering relative tothe other activities based on, for example, a pre-set numericalmultiplicative value (e.g., “1.8×”), meaning that an original responseratio of 50% responses relating to mountaineering with 10% to the othersports, respectively, may be altered by the multipliers to a final ratioof 90% emphasis placed on mountaineering, with an even 2% each acrossthe remaining sports. As a result, processor 144A may thereby elect toretrieve additional digital media interface query data structure contentfrom database 132 relating to mountaineering at a heightened emphasis(e.g., 90%) relative to the original 50%, given that earlier user inputindicates a higher interest in that particular sport or activity. Thedescribed examples are for illustrative purposes only in that a personskilled in the art would recognize other calculative and/ormultiplicative ratios or procedures as suitable upon review of theentirety of this disclosure. Next, machine-learning module 124A mayperform data multiplier scoring 136A between user responses to interfacequery data structure recommendation 116A to organize data multipliersgenerated in data multiplier generation 120A into an orderedhierarchical list. Returning to the example relating to mountaineering,in one or more non-limiting embodiments, multipliers relating tomountaineering (e.g., in increasing the relative emphasis placed onmountaineering relative to other categories) may be scored higher andplaced at a top end of a pre-defined range, e.g., 1-10, where “1”represents no correlation with mountaineering and “10” representsmaximum correlation with mountaineering. In addition, in one or moreembodiments, multipliers may include data describing the next three (3)or more achievements will improve the pride, confidence, and/orexcitement of the user. In the context of mountaineering, this may meandisplaying indicia on display device 128 relating to challenging theuser further only if they successfully complete certain identifiedpredecessor hikes or climbs, e.g., climbing Mount Blanc prior toclimbing Mount Everest or K2. As a result, processor 144A may performstrategy data generation 138A based on interface query data structure112A and data multiplier generation 120A and/or data multiplier scoring136.

For the purposes of this disclosure, a “strategy data generation” refersto generation of one or more strategies presented in an orderedhierarchy based on relevancy to the user progressing towards theiridentified achievement goals. If the user is considering pursuingmultiple activities, such as adventure sports hobbies as describedabove, then strategy data generation 138A may consider and computestrategies based on quantitatively manipulating data describing any oneor more of user datum 108, interface query data structure 112, and datamultiplier scoring 136. A person skilled in the art would recognize thatany particular calculative and/or multiplicative procedure would besuitable upon review of the entirety of this disclosure for processor144A to complete strategy data generation 138. For example, in one ormore embodiments, strategies produced by strategy data generation 138Amay be ranked by processor 144A so that the user may determine whichstrategy is most relevant with attaining their goals. Strategy datageneration 138A may include machine-learning processes that are used tocalculate one or more strategies, e.g., a set of strategies, eachcorresponding to assisting the user progress toward attaining theirgoals.

In one or more embodiments, a machine-learning process may be used togenerate one or more strategies relating to improving user performancein an area or field of interest or to generate a machine-learning modelfor strategy data generation 138. In one or more embodiments, amachine-learning model may be generated using training data. Trainingdata may include inputs and corresponding predetermined outputs so thata machine-learning module may use the correlations between the providedexemplary inputs and outputs to develop an algorithm and/or relationshipthat then allows the machine-learning module to determine its ownoutputs for inputs. Training data may contain correlations that amachine-learning process may use to model relationships between two ormore categories of data elements. The exemplary inputs and outputs maycome from database 132 and/or as any database described in thisdisclosure or be provided by the user. In other embodiments, amachine-learning module may obtain a training set by querying acommunicatively connected database that includes past inputs andoutputs. Training data may include inputs from various types ofdatabases, resources, and/or user inputs and outputs correlated to eachof those inputs so that a machine-learning module may determine anoutput, such as a personal performance data output associated with orotherwise generated by strategy data generation 138, for an input, suchas interface query data structure 112A and user datum 108. Correlationsmay indicate causative and/or predictive links between data, which maybe modeled as relationships, such as mathematical relationships, bymachine-learning processes, as described in further detail below. In oneor more embodiments, training data may be formatted and/or organized bycategories of data elements by, for example, associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements.

In one or more embodiments, interface query data structurerecommendation 116A may include information from interface query datastructure 112A and user datum 108A for iteratively revising interfacequery data structure recommendation 116A and strategy data generation138. As a result, strategy data generation 138A may provide a one ormore strategies responsive to user datum 108A and/or interface querydata structure 112. In one or more embodiments, interface query datastructure recommendation 116A may include a video component, audiocomponents, text components, and combination thereof, and the like. Asused in this disclosure, a “digital media interface query datastructure” is an interface query data structure provided in digitalmedia format (e.g., digital videos, digital photos, etc.) to, forexample, receive verbal responses to a sequence of targeted questioningrelating to a particular area or field in which the user is seeking toimprove their performance, such as for a particular activity. In somecases, digital media interface query data structures may include contentthat is representative or communicative of an at least attribute of asubject, such as a user. As used in this disclosure, a “subject” is aperson such as, for example an aspiring alpinist. Subject user may berepresented by, for example, their video-recorded verbal responses or bydigital photos. For example, in some cases, an image component of adigital media resume may include an image of a subject. As used in thisdisclosure, an “image component” may be a visual representation ofinformation, such as a plurality of temporally sequential frames and/orpictures, related to video resume and target video resume. For example,image component may include animations, still imagery, recorded video,and the like. Attributes may include subject's skills, competencies,credentials, talents, and the like. In some cases, attributes may beexplicitly conveyed within video-recorded responses to a video interfacequery data structure and/or user-uploaded digital photos. Alternatively,or additionally, in some cases, attributes may be conveyed implicitlywithin a video interface query data structure or video-recordedresponses thereto. Video resume may include a digital video. Digitalvideo may be compressed to optimize speed and/or cost of transmission ofvideo. Videos may be compressed according to a video compression codingformat (i.e., codec). Exemplary video compression codecs include H.26xcodecs, MPEG formats, VVC, SVT-AV1, and the like. In some cases,compression of a digital video may be lossy, in which some informationmay be lost during compression. Alternatively, or additionally, in somecases, compression of a digital video may be substantially lossless,where substantially no information is lost during compression.

In some cases, computing device 104A may include audiovisual speechrecognition (AVSR) processes to recognize verbal content in a videointerface query data structure. For example, computing device 104A mayuse image content to aid in recognition of audible verbal content suchas viewing user move their lips to speak on video to process the audiocontent of video-recorded responses to a vide interface query datastructure. AVSR may use image component to aid the overall translationof the audio verbal content of video resumes. In some embodiments, AVSRmay include techniques employing image processing capabilities in lipreading to aid speech recognition processes. In some cases, AVSR may beused to decode (i.e., recognize) indeterministic phonemes. In somecases, AVSR may include an audio-based automatic speech recognitionprocess and an image-based automatic speech recognition process. AVSRmay combine results from both processes with feature fusion. Audio-basedspeech recognition process may analysis audio according to any methoddescribed herein, for instance using a Mel frequency cepstralcoefficient (MFCCs) and/or log-Mel spectrogram derived from raw audiosamples. Image-based speech recognition may perform feature recognitionto yield an image vector. In some cases, feature recognition may includeany feature recognition process described in this disclosure, forexample a variant of a convolutional neural network. In some cases, AVSRemploys both an audio datum and an image datum to recognize verbalcontent. For instance, audio vector and image vector may each beconcatenated and used to predict speech made by a user, who is “oncamera.”

In some cases, computing device 104A may be configured to recognize atleast a keyword as a function of visual verbal content. In some cases,recognizing at least keyword may include an optical characterrecognition (OCR). In some cases, computing device 104A may transcribemuch or even substantially all verbal content from video-recordedresponses to a video interface query data structure. Alternatively,computing device 104A may use OCR and/or intelligent characterrecognition (ICR) may recognize written text one glyph or character at atime, for instance by employing machine-learning processes. In somecases, intelligent word recognition (IWR) may recognize written text,one word at a time, for instance by employing machine-learning processesin a variety of user-uploaded digital content, including videos, photos,scans of documents with text and/or the like.

Still referring to FIG. 1 , in some cases, OCR may includepost-processing. For example, OCR accuracy may be increased, in somecases, if output is constrained by a lexicon. A lexicon may include alist or set of words that are allowed to occur in a document. In somecases, a lexicon may include, for instance, all the words in the Englishlanguage, or a more technical lexicon for a specific field. In somecases, an output stream may be a plain text stream or file ofcharacters. In some cases, an OCR process may preserve an originallayout of visual verbal content. In some cases, near-neighbor analysiscan make use of co-occurrence frequencies to correct errors, by notingthat certain words are often seen together. For example, “Washington,D.C.” is generally far more common in English than “Washington DOC.” Insome cases, an OCR process may make us of a prior knowledge of grammarfor a language being recognized. For example, grammar rules may be usedto help determine if a word is likely to be a verb or a noun. Distanceconceptualization may be employed for recognition and classification.For example, a Levenshtein distance algorithm may be used in OCRpost-processing to further optimize results.

In one or more embodiments, apparatus 100 may further include a memorycomponent 140. Memory component 140 may be communicatively connected tocomputing device 104A and may be configured to store information and/ordatum related to apparatus 100, such as interface query data structure112, user datum 108, information related to interface query datastructure recommendation 116, information related to data multipliergeneration 120, and the like. In one or more embodiments, memorycomponent 140 is communicatively connected to processor 144A andconfigured to contain instructions configuring processor to determinethe record recommendation. Memory component 140 may be configured tostore information, datum, and/or elements of data related to postingmatch recommendation. For example, memory component 140 may storepreviously prepared records (e.g., video recordings of user responses tovideo interface query data structures, user-uploaded photos, etc.),customized records generated by computing device 104, interface querydata structure 112, user datum 108, data multiplier generation 120,interface query data structure recommendation 116, and/or the like. Inone or more embodiments, memory component 140 may include a storagedevice, as described further in this disclosure below.

In one or more embodiments, display device 128 may be communicativelyconnected to computing device 104. Display device may be remote tocomputing device or integrated into computing device 104. Communicationbetween computing device 104A and display component may be wired orwireless. In one or more embodiments, display device 128 may beconfigured to display user datum 108, interface query data structure112, interface query data structure recommendation 116, data multipliergeneration 120, data multiplier scoring 136, data describing database132, and/or the like. Display device 128 may include a graphic userinterface (GUI) that a user may use to navigate through presented dataor information by computing device 104. In one or more embodiments, aGUI may include a plurality of lines, images, symbols, and the like toshow information and/or data. In addition, the GUI may be configured toprovide an articulated graphical display on display device, thearticulated graphical display including multiple regions, each regionproviding one or more instances the point of interaction between theuser and the remote display device. In non-limiting embodiments, displaydevice 128 may include a smartphone, tablet, laptop, desktop, monitor,tablet, touchscreen, head-up display (HUD), and the like. In one or moreembodiments, display device 128 may include a screen such as a liquidcrystal display (LCD) various other types of displays or monitors, aspreviously mentioned in this disclosure. In one or more embodiments,user may view information and/or data displayed on display device 128 inreal time. In one or more embodiments, display component may beconfigured to display received or determined information, which may betoggled through using, for example, an input device of display componentor computing device 104. Display device 128 may include electroniccomponents utilized to display image data or information, such as avideo, GUI, photo, and the like.

Referring to FIGS. 3A-3D, example output screens 300A-300D displayingoutput generated by interface query data structure 112A are shown,respectively. As defined earlier, an “interface query data structure”refers to, for example, a data organization format used to digitallyrequest a data result or action on the data (e.g., stored in adatabase). In one or more embodiments, each output screen 300A-300D maybe an example of output screen configured to be displayed by displaydevice 128 of FIG. 1 by interface query data structure 112A. That is,more particularly, interface query data structure 112A may configuredisplay device 128 of FIG. 1 to display any one or more of outputscreens 300A-300D as described in the present disclosure. Accordingly,output screen 300A may include multiple forms of indicia, includingcategory identification 304A, screen type 308A, content display area312A, and interactivity components 316A-324A. In one or moreembodiments, category identification 304A may include an identificationof a category (e.g., selected by processor 144A from query database 200)intended for display for subsequent interaction with the user. Forexample, output screen 300A may display questions associated with aninterface query data structure from a query from morale category 204 ofFIG. 2 and thereby display “Morale Category: Question 1 of 4.” Thedescribed examples are for illustrative purposes only in that a personskilled in the art would recognize the selection of other categories(e.g., including those not listed as exampled in query database 200 ofFIG. 2 ), questions, number of questions, and content delivery type(e.g., textual questions, digital photo interactivity, digital videointeractivity, etc.) as suitable upon review of the entirety of thisdisclosure.

In the example shown by output screen 300A of FIG. 3A, categoryidentification 304A indicates a question number and date. Screen type308A corresponds with the question number shown in categoryidentification 304A. Referring now to a particular field of interest toa user for improving personal performance (e.g., overcoming OCD),content display area 312A may include, for example, textual questions orphrases relating to evaluation of past achievements. The user may inputtheir selection by touching any one of interactivity components316A-324A for processor 144A to intake the user's selection andcorrespondingly iteratively update interface query data structure 112Aas described earlier, if needed or preferred. As a result, output screen300B, which may be displayed in response to user-provided input bytouching any one of interactivity components 316A-324A, may includeoutput screen 308B relating to “Strategies for ImprovingObsessive-Compulsive Disorder (OCD).” In one or more embodiments,display screen type 308B may include one or more specific strategies(e.g., displayed in content display area 312B) that are tailored toassist the user to attain their specified achievement goal, such as“exercise regularly,” and the user may select this option (e.g., bytouching it on output screen 308B).

Computing device 104A may then intake, evaluate and/or processuser-selection of, for example, data describing “exercise regularly” asa part of one or more of interface query data structure recommendation116, data multiplier generation 120, machine learning module 124, datamultiplier scoring 136A and/or strategy data generation 138A asdescribed earlier. For example, output screen 300C may show displayoutput screen 308C relating to “User-Input Progress Ranking,” which maycorrespond to strategy data generation 136A as described earlier. Thatis, in one or more embodiments, the user may provide additional input(e.g., relating to ranking of their progress with the suggestedstrategy) responsive to strategy data generation 138A for additionaliterative refinement relating how often or regularly the user adheres tothe earlier displayed strategy (e.g., “exercise regularly”). In one ormore embodiments, user input may include a description of specificactions a user is taking in response to a strategy, such as how, when,where, and frequency of the action. Input provided by the user incontent display area 312C may then be evaluated by described processesperformed by processor 144A to show output screen 300D including displayscreen type 308C with “Personal Performance Data for Option No. 2:Exercise Regularly” and graph 316D included in content display area312D. Graph 316D may be indicative of data multiplier scoring 136Aand/or strategy data generation 138A as performed by processor 144A ofFIG. 1 . In addition, graph 316D may show user performance, e.g.,frequency of weekly exercise visits per month over several months, basedon processor 144A receiving user input through display device 128. Thatis, in one or more embodiments, processor 144A may use an inferenceengine or machine learning to assess progress of the user and userelated data to amend or generate new strategies through strategy datageneration 138A based on user progress. In addition, the processor mayassess how often/long the user adheres to a suggested strategy, and whatspecific actions (e.g., exercising 3× per week) have the most positiveimpact, and the like. In addition, the processor may assess andrecalibrate strategies generated by strategy data generation 138A on aperiodic basis, e.g., a monthly, quarterly, or yearly basis.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. In one or more embodiments,machine-learning module 400 may be an example of machine learning module124A of computing device 104A of FIG. 1 . Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 404 togenerate an algorithm that will be performed by a computingdevice/module (e.g., computing device 104A of FIG. 1 ) to produceoutputs 408 given data provided as inputs 412; this is in contrast to anon-machine learning software program where the commands to be executedare determined in advance by a user and written in a programminglanguage.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include multipledata entries, each entry representing a set of data elements that wererecorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training data404 may evince one or more trends in correlations between categories ofdata elements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data 404 according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data 404 may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data 404 may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data 404 may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data 404 may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively, or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, input data may include user datum 108A and/or interface querydata structure 112, which may be at least in part based on user datum108A to provide a personal performance data output (e.g., graph 316D ofFIG. 3D). In one or more embodiments, interface query data structure112A includes one or more interface query data structures, any one ofwhich may include an interface that defines a set of operationssupported by a data structure and related semantics, or meaning, ofthose operations. For example, in the context of personal performanceimprovement coaching, interface query data structure 112A may includeone or more interface query data structures that may appear to the userin the form of one or more text-based or other digital media-basedsurveys, questionnaires, lists of questions, examinations, descriptions,etc., any one of which may include categorical questions in one or morediscrete categories including morale, momentum, motivation, andmultipliers.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. A distance metric may include any norm,such as, without limitation, a Pythagorean norm. Machine-learning module400 may generate a classifier using a classification algorithm, definedas a process whereby a computing device and/or any module and/orcomponent operating thereon derives a classifier from training data 404.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers. As anon-limiting example, training data classifier 416 may classify elementsof training data to iteratively refine strategies generated by strategydata generation 138A to reflect the user's preferences more accuratelyfor improving their performance to attain their achievement goal. Thatis, in one or more embodiments, in the context of improvingmountaineering skill, training data may include providing multiplemedium-grade intensity trails or routes to the user to receive theirfeedback regarding perceived intensity or difficulty. Such providedroutes may be subsequently and iteratively refined based on input userprior performance capabilities, such as hiking prior to climbing in theHimalayas. This input may then be analyzed by machine learning module124A and allow strategy data generation 138A to generate appropriatestrategies. For example, such strategies may be directed to improve userperformance based on user responses to queries generated from trainingdata. As a result, more experienced hikers and alpinists will becontinually guided and challenged with appropriate feedback generated bystrategy data generation 138.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively, or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum (e.g., a personal performance data outputfor improving a confidence level of the user). As a further non-limitingexample, a machine-learning model 424 may be generated by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdata 404 set are applied to the input nodes, a suitable trainingalgorithm (such as Levenberg-Marquardt, conjugate gradient, simulatedannealing, or other algorithms) is then used to adjust the connectionsand weights between nodes in adjacent layers of the neural network toproduce the desired values at the output nodes. This process issometimes referred to as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude user datum 108A and/or interface query data structure 112A asdescribed above as inputs, graph 316D and/or similar textual and/orvisual imagery (e.g., digital photos and/or videos) relating toproviding a personal performance data output for improving a confidencelevel of a user as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 404. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 428 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminant analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized trees, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring to FIG. 5 , an exemplary embodiment of fuzzy set comparison500 is illustrated. In one or more embodiments, data describing anydescribed process relating to providing a personal performance dataoutput (e.g., for improving a confidence level of a user) as performedby processor 144A of computing device 104A may include data manipulationor processing including fuzzy set comparison 500. In addition, in one ormore embodiments, usage of an inference engine relating to datamanipulation may involve one or more aspects of fuzzy set comparison 500as described herein. That is, although discrete integer values may beused as data to describe, for example, user datum 108A and/or interfacequery data structure 112, fuzzy set comparison 500 may be alternativelyused. For example, a first fuzzy set 504 may be represented, withoutlimitation, according to a first membership function 508 representing aprobability that an input falling on a first range of values 512 is amember of the first fuzzy set 504, where the first membership function508 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function508 may represent a set of values within first fuzzy set 504. Althoughfirst range of values 512 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 512 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 508 mayinclude any suitable function mapping first range of values 512 to aprobability interval, including without limitation a triangular functiondefined by two linear elements such as line segments or planes thatintersect at or below the top of the probability interval. As anon-limiting example, triangular membership function may be defined as:

${y( {x,a,b,c} )} = \{ \begin{matrix}{0,\ {{{for}\ x} > {c\ {and}{\ }x} < a}} \\{\frac{x - a}{b - a},\ {{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},\ {{{if}{\ }b} < x \leq c}}\end{matrix} $a trapezoidal membership function may be defined as:

${y( {x,a,b,c,d} )} = {\max( {{\min\ ( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} )},\ 0} )}$a sigmoidal function may be defined as:

${y( {x,a,c} )} = \frac{1}{1 - e^{- {a({x - c})}}}$a Gaussian membership function may be defined as:

${y( {x,c,\sigma} )} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$and a bell membership function may be defined as:

${y( {x,a,b,c,} )} = \lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \rbrack^{- 1}$Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 5 , first fuzzy set 504 may represent any valueor combination of values as described above, including output from oneor more machine-learning models, user datum 108A and/or interface querydata structure 112, and a predetermined class, such as withoutlimitation, query data or information including interface query datastructures stored in query database 200 of FIG. 2 . A second fuzzy set516, which may represent any value which may be represented by firstfuzzy set 504, may be defined by a second membership function 520 on asecond range of values 524; second range of values 524 may be identicaland/or overlap with first range of values 512 and/or may be combinedwith first range via Cartesian product or the like to generate a mappingpermitting evaluation overlap of first fuzzy set 504 and second fuzzyset 516. Where first fuzzy set 504 and second fuzzy set 516 have aregion 528 that overlaps, first membership function 508 and secondmembership function 520 may intersect at a point 532 representing aprobability, as defined on probability interval, of a match betweenfirst fuzzy set 504 and second fuzzy set 516. Alternatively, oradditionally, a single value of first and/or second fuzzy set may belocated at a locus 536 on first range of values 512 and/or second rangeof values 524, where a probability of membership may be taken byevaluation of first membership function 508 and/or second membershipfunction 520 at that range point. A probability at 528 and/or 532 may becompared to a threshold 540 to determine whether a positive match isindicated. Threshold 540 may, in a non-limiting example, represent adegree of match between first fuzzy set 504 and second fuzzy set 516,and/or single values therein with each other or with either set, whichis sufficient for purposes of the matching process; for instance,threshold may indicate a sufficient degree of overlap between an outputfrom one or more machine-learning models and/or user datum 108A and/orinterface query data structure 112 and a predetermined class, such aswithout limitation, query data categorization, for combination to occuras described above. Alternatively, or additionally, each threshold maybe tuned by a machine-learning and/or statistical process, for instanceand without limitation as described in further detail below.

Further referring to FIG. 5 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify user datum 108A and/orinterface query data structure 112A with interface query data structuredata stored in query database 200. For instance, if a user datum 108Aand/or interface query data structure 112A has a fuzzy set matchingcertain interface query data structure data values stored in querydatabase 200 (e.g., by having a degree of overlap exceeding athreshold), computing device 104A may classify the user datum 108Aand/or interface query data structure 112A as belonging to querycategorization (e.g., generating strategies by strategy data generation138A based at least in part on user-provided responses to interfacequery data structure recommendation 116). Where multiple fuzzy matchesare performed, degrees of match for each respective fuzzy set may becomputed and aggregated through, for instance, addition, averaging, orthe like, to determine an overall degree of match.

Still referring to FIG. 5 , in an embodiment, user datum 108A and/orinterface query data structure 112A may be compared to multiple querydatabase 200 categorization fuzzy sets. For instance, user datum 108Aand/or interface query data structure 112A may be represented by a fuzzyset that is compared to each of the multiple query database 200categorization fuzzy sets; and a degree of overlap exceeding a thresholdbetween the user datum 108A and/or interface query data structure 112Afuzzy set and any of the multiple query database 200 categorizationfuzzy sets may cause computing device 104A to classify the user datum108A and/or interface query data structure 112A as belonging to one ormore corresponding interface query data structures associated with querydatabase 200 categorization (e.g., selection from morale category 204,etc.). For instance, in one embodiment there may be two query database200 categorization fuzzy sets, representing, respectively, querydatabase 200 categorization (e.g., into each of morale category 204,momentum category 208, motivation category 212, and multipliers 216).For example, a First query database 200 categorization may have a firstfuzzy set; a Second query database 200 categorization may have a secondfuzzy set; and user datum 108A and/or interface query data structure112A may each have a corresponding fuzzy set. Computing device 104, forexample, may compare an user datum 108A and/or interface query datastructure 112A fuzzy set with fuzzy set data describing each of thecategories included query database 200, as described above, and classifya user datum 108A and/or interface query data structure 112A to one ormore categories (e.g., into each of morale category 204, momentumcategory 208, motivation category 212, and multipliers 216).Machine-learning methods as described throughout may, in a non-limitingexample, generate coefficients used in fuzzy set equations as describedabove, such as without limitation x, c, and σ of a Gaussian set asdescribed above, as outputs of machine-learning methods. Likewise, userdatum 108A and/or interface query data structure 112A may be usedindirectly to determine a fuzzy set, as user datum 108A fuzzy set and/orinterface query data structure 112A fuzzy set may be derived fromoutputs of one or more machine-learning models that take the user datum108A and/or interface query data structure 112A directly or indirectlyas inputs.

Still referring to FIG. 5 , a computing device may use a logiccomparison program, such as, but not limited to, a fuzzy logic model todetermine a query database 200 response. A query database 200 responsemay include, but is not limited to, morale category 204, momentumcategory 208, motivation category 212, and multipliers 216, and thelike; each such query database 200 response may be represented as avalue for a linguistic variable representing query database 200 responseor in other words a fuzzy set as described above that corresponds to adegree of matching between data describing user datum 108A and/orinterface query data structure 112A and one or more categories withinquery database 200 as calculated using any statistical,machine-learning, or other method that may occur to a person skilled inthe art upon reviewing the entirety of this disclosure. In someembodiments, determining a query database 200 categorization may includeusing a linear regression model. A linear regression model may include amachine learning model. A linear regression model may be configured tomap data of user datum 108A and/or interface query data structure 112,to one or more query database 200 parameters. A linear regression modelmay be trained using a machine learning process. A linear regressionmodel may map statistics such as, but not limited to, quality of userdatum 108A and/or interface query data structure 112. In someembodiments, determining query database 200 of user datum 108A and/orinterface query data structure 112A may include using a query database200 classification model. A query database 200 classification model maybe configured to input collected data and cluster data to a centroidbased on, but not limited to, frequency of appearance, linguisticindicators of quality, and the like. Centroids may include scoresassigned to them such that quality of user datum 108A and/or interfacequery data structure 112A may each be assigned a score. In someembodiments, query database 200 classification model may include aK-means clustering model. In some embodiments, query database 200classification model may include a particle swarm optimization model. Insome embodiments, determining the query database 200 of user datum 108Aand/or interface query data structure 112A may include using a fuzzyinference engine (e.g., to assess the progress of the user and use saiddata to amend or generate new strategies based on user progress). Afuzzy inference engine may be configured to map one or more user datum108A and/or interface query data structure 112A data elements usingfuzzy logic. In some embodiments, user datum 108A and/or interface querydata structure 112A may be arranged by a logic comparison program intoquery database 200 arrangement. A “query database 200 arrangement” asused in this disclosure is any grouping of objects and/or data based onskill level and/or output score as defined by, for example, datamultiplier scoring 136. This step may be implemented as described abovein FIG. 1 . Membership function coefficients and/or constants asdescribed above may be tuned according to classification and/orclustering algorithms. For instance, and without limitation, aclustering algorithm may determine a Gaussian or other distribution ofquestions about a centroid corresponding to a given scoring level, andan iterative or other method may be used to find a membership function,for any membership function type as described above, that minimizes anaverage error from the statistically determined distribution, such that,for instance, a triangular or Gaussian membership function about acentroid representing a center of the distribution that most closelymatches the distribution. Error functions to be minimized, and/ormethods of minimization, may be performed without limitation accordingto any error function and/or error function minimization process and/ormethod as described in this disclosure.

Further referring to FIG. 5 , an inference engine may be implemented toassess the progress of the user and use said data to amend or generatenew strategies based on user progress according to input and/or outputmembership functions and/or linguistic variables. For instance, a firstlinguistic variable may represent a first measurable value pertaining touser datum 108A and/or interface query data structure 112, such as adegree of matching between data describing user aspirations andstrategies based on responses to interface query data structures storedin query database 200. Continuing the example, an output linguisticvariable may represent, without limitation, a score value. An inferenceengine may combine rules, such as: “if the difficulty level of aparticular activity (e.g., mountaineering) is ‘hard’ and the popularitylevel is ‘high’, the question score is ‘high’”—the degree to which agiven input function membership matches a given rule may be determinedby a triangular norm or “T-norm” of the rule or output membershipfunction with the input membership function, such as min (a, b), productof a and b, drastic product of a and b, Hamacher product of a and b, orthe like, satisfying the rules of commutativity (T(a, b)=T(b, a)),monotonicity: (T(a, b)≤T(c, d) if a≤c and b≤d), (associativity: T(a,T(b, c))=T(T(a, b), c)), and the requirement that the number 1 acts asan identity element. Combinations of rules (“and” or “or” combination ofrule membership determinations) may be performed using any T-conorm, asrepresented by an inverted T symbol or “⊥,” such as max(a, b),probabilistic sum of a and b (a+b−a*b), bounded sum, and/or drasticT-conorm; any T-conorm may be used that satisfies the properties ofcommutativity: ⊥(a, b)=⊥(b, a), monotonicity: ⊥(a, b)<⊥(c, d) if a≤c andb≤d, associativity: ⊥(a, ⊥(b, c))=⊥(⊥(a, b), c), and identity element of0. Alternatively, or additionally T-conorm may be approximated by sum,as in a “product-sum” inference engine in which T-norm is product andT-conorm is sum. A final output score or other fuzzy inference outputmay be determined from an output membership function as described aboveusing any suitable defuzzification process, including without limitationMean of Max defuzzification, Centroid of Area/Center of Gravitydefuzzification, Center Average defuzzification, Bisector of Areadefuzzification, or the like. Alternatively, or additionally, outputrules may be replaced with functions according to the Takagi-Sugeno-King(TSK) fuzzy model.

Further referring to FIG. 5 , user datum 108A and/or interface querydata structure 112A to be used may be selected by user selection, and/orby selection of a distribution of output scores, such as 50%hard/expert, 40% moderate average, and 50% easy/beginner levels or thelike. Each query database 200 categorization may be selected using anadditional function such as in query database 200 as described above.

Now referring to FIG. 6 , a method 600 for providing a personalperformance data output for improving a confidence level of a user ispresented. At step 605, method 600 includes receiving, by computingdevice 104, user datum 108, which may be a query including a survey,questionnaire and/or the like (e.g., generated by strategy datageneration 138). In addition, the survey may be a representation ofinterface query data structure 112A displayed to the user. In one ormore embodiments, the survey may include categorical questions in one ormore discrete categories including morale, momentum, motivation, andmultipliers in areas pertaining to user defined interests, such as thatshown and described for FIGS. 1B-1C. In addition, the survey may begenerated based on receiving a user data (e.g., user datum 108) andthereby correspondingly retrieved from relevant data in query database200 of FIG. 2 .

For example, the survey may be presented in textual and/or ininteractive digital media (e.g., digital video and/or photos) and beselected from one or more categories within query database 200 (e.g.,morale category 204, momentum category 208, and/or motivation category212, etc.). In addition, in one or more embodiments, data describingresponses to a may be multiplied by multipliers 216 by data multipliergeneration 120A for further data processing and manipulation, etc. Thisstep may be implemented as described above, without limitation, in FIGS.1-7 .

Still referring to FIG. 6 , at step 610, method 600 includes generating,by computing device 104, interface query data structure 112A of FIG. 1A,which may include at least a query including an input field (e.g., userinput field 130A) based on user datum 108A as shown and described forFIG. 1A. In some instances, user input field 130A may include responsesinterface query data structure 112 in the form of one or more of textualor visual responses provided by the user to, for example, eachcategorical question of a survey included in interface query datastructure 112A. Interface query data structure 112A may be provided bythe user to computing device 104A by any processes and method describedearlier (e.g., user-input into a touch-screen interface of a digitalperipheral or device, voice and/or video recognition, dictationtranscription, etc.). In one or more embodiments, interface query datastructure 112A may be used by one or more of interface query datastructure recommendation, data multiplier generation 120, machinelearning module 124, data multiplier scoring 136A and/or strategy datageneration 138A as described earlier. That is, interface query datastructure 112A may be used to generate an interface query data structureusing interface query data structure recommendation 116, which mayaccess database 132 and/or query database 200 of FIG. 2 to retrieve oneor more queries including interface query data structures that, forexample, correlate with or match user datum 108. This step may beimplemented as described above, without limitation, in FIGS. 1-7 .

Still referring to FIG. 6 , at step 615, method 600 includes generating,by computing device 104, multiple data multipliers (e.g., multipliers216) based at least in part on a first user-input datum (e.g., providedin user input field 130A of FIG. 1A) that may include textual or visualresponses to each questions posed to the user by interface query datastructure 112A. For example, the user may provide a text-based responseor upload photos corresponding to a respective question in a interfacequery data structure such that processor 144A may use machine-learningprocess such as optical character recognition to assess data received.In addition, in one or more embodiments, each data multiplier mayinclude achievement-related data values. At least one of the datamultipliers may be generated using machine-learning module 124, whichmay be configured to generate data multipliers using user datum 108Aand/or interface query data structure 112. For example, data multipliersmay be used in data multiplier scoring 136A by processor 144A toemphasize data describing aspirational interests of the user to, forexample, more accurately track those interests. As described earlier,data multipliers may be used as multiplicative factors to increaserelative emphasis on data describing aspirations of higher interest tothe user than other goals or objectives. As a result, data multipliersmay be used in data multiplier scoring 136A to, for example, provide apersonal performance data output (e.g., graph 316D) tailoredspecifically to those user interests. This step may be implemented asdescribed above, without limitation, in FIGS. 1-7 .

Still referring to FIG. 6 , at step 620, method 600 includes generating,by the computing device, a strategy data based on the first user-inputdatum and multiple data multipliers, the strategy data including a datamultiplier instruction configured to further multiply at least some datamultipliers based on an ordered list. For example, each data multipliermay include multiple data values and at least some data multipliers maybe generated and scored using a machine learning model (e.g., run bymachine learning module 124A of FIG. 1A) including a classifierconfigured to correlate user datum 108A to interface query datastructure 112A to correspondingly multiply at least some datamultipliers into an ordered list based on the score. In one or moreembodiments, the strategy data may describe a task or step for the userto reach a future achievement milestone and/or to assist the user reacha data multiplier. In addition, processor 144A may use machine-learningmodule 124A including a classifier to generate the strategy data. As aresult, elements of the interface query data structure 112A and datamultipliers may be optionally classified into strategy data values usingthe classifier. In addition, or the alternative, strategy data may begenerated by strategy data generation 138A as described earlier and bebased on processing user datum 108A and/or interface query datastructure 112A by, for example, machine learning module 124A and datamultiplier scoring 136. In one or more embodiments, strategy data mayinclude data describing one or more particular strategies relating toaddressing user-defined achievement goals in, for example, user datum108A and/or interface query data structure 112. As a result, strategydata may be output in the form of textual summaries, visual depictions(e.g., digital photos, depictions, etc.) tailored to providing a planfor user performance improvement.

For example, in the context of mountaineering, strategy data may includespecific training regimens, such as high-altitude climateacclimatization prior to preparatory hikes, stretching routines, dietaryrecommendations, heart rate tracking and/or the like. In addition,strategy data may be continuously or periodically updated by, forexample, any described process performed by processor 144A to, forexample, accurately track and guide user goals, which may change overtime. That is, a user may initially seek to climb several mountains, butthen after achieving this goal, may want to reduce physical intensity toreturn to only hiking. Strategy data generated by strategy datageneration 138A may take this into account, since data multipliergeneration 120A may, over time, diminish significance of climbingpreferences relative to hiking preferences. Accordingly, data describingclimbing relative to hiking will also be proportionately diminishedresulting in personal performance data output describing hiking. Thisstep may be implemented as described above, without limitation, in FIGS.1-7 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay device 736, discussed further below. Input device 732 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Video display adapter 752 and display device 736 may beutilized in combination with processor 704 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 700 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 712 via a peripheral interface 756.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,apparatus, and software according to the present disclosure.Accordingly, this description is meant to be taken only by way ofexample, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for tracking progress of measuredphenomena, the apparatus comprising: at least a processor; a memorycommunicatively connected to the processor, the memory containinginstructions configuring the at least a processor to: receive a userdatum; generate an interface query data structure including at least aquery including an input field based on the user datum, wherein theinterface query data structure configures a remote display device to:display the input field to the user; receive at least a first user-inputdatum into the input field; retrieve data describing attributes of theuser from a database communicatively connected with the processor; andrefine the interface query data structure based on data describingattributes of the user from the database; generate multiple datamultipliers based on the first user-input datum, wherein: each datamultiplier includes multiple data values including relatively higherdata values describing data indicative of progress of the user towardmatching a target; and at least some data multipliers are generated andscored using a machine learning model including a classifier configuredto correlate the user datum and the first user-input datum to datadescribing the target into an ordered list based on score; generate astrategy data for the user based on the first user-input datum,relatively higher data values, and the ordered list, wherein: thestrategy data is generated in response to a data multiplier instructiondefined as multiplying at least the first user-input datum by relativelyhigher data values and displaying feedback to the user according to theordered list; and the interface query data structure configures theremote display device to: display at least the strategy data; receive asecond user-input datum through the remote display device correspondingto at least some aspects of the strategy data, the second user-inputdatum demonstrating either a similarity or a dissimilarity to the firstuser-input datum, the strategy data configured to be iterativelyrecalculated based on the second user-input datum by at least themachine learning model; and display the performance data output to theuser.
 2. The apparatus of claim 1, wherein the interface query datastructure is at least partially based on data describing attributes ofthe user that are retrieved from the database including categoricalinformation correlated to a historical range of data.
 3. The apparatusof claim 2, wherein the second user-input datum comprises datadescribing current preferences of the user and the classifier of themachine learning model is configured to correlate the user datum, thefirst user-input datum, and the second user-input datum to datadescribing the target and iteratively generate the ordered list betweena minimum value and a maximum value.
 4. The apparatus of claim 1,wherein the machine learning model is configured to apply an informationmomentum multiplier to the user datum and/or the interface query datastructure, wherein the information momentum multiplier is defined by thesecond user-input datum exceeding a pre-defined numerical threshold. 5.The apparatus of claim 1, wherein at least some attributes retrievedfrom the database describe data relating to motivation of the userdefined as a frequency of completing activities related to the strategydata.
 6. The apparatus of claim 1, wherein if the second user-inputdatum demonstrates the dissimilarity to the first user-input datum, themachine learning model iteratively recalculates the strategy datareflective of the dissimilarity such that the strategy data includesdata describing relatively more of the second user-input datum than thefirst user-input datum.
 7. The apparatus of claim 1, wherein the remotedisplay device is configured to display the input field to the user by aGraphical User Interface (GUI) defined as a point of interaction betweenthe user and the remote display device.
 8. The apparatus of claim 7,wherein the GUI provides an articulated graphical display on the remotedisplay device, the articulated graphical display including multipleregions, each region providing one or more instances the point ofinteraction between the user and the remote display device.
 9. Theapparatus of claim 1, wherein the machine learning model is configuredto perform optical character recognition and process data associatedwith the first user-input datum and/or the second user-input datum. 10.The apparatus of claim 1, wherein query interface data structure furtherconfigures the remote display device to display the performance dataoutput as a function of a user data change descriptor generated based onthe second user-input datum.
 11. The apparatus of claim 1, wherein thefirst user-input datum and/or the second user-input datum comprises atleast an element of data describing a user-responsiveness factor definedas a frequency of the user in completing activities associated with theperformance data output.
 12. The apparatus of claim 1, wherein theperformance data output is iteratively updated by the classifier of themachine learning model, which is configured to classify data describinga frequency of the user completing activities describing progress of theuser toward matching the target to the strategy data.
 13. The apparatusof claim 1, wherein the processor is configured to update the strategydata based on the second user-input datum at periodic intervals.
 14. Amethod for tracking progress of measured phenomena, the methodcomprising: receiving, by a computing device, a user datum; generating,by the computing device, an interface query data structure including atleast a query including an input field based on the user datum, whereinthe interface query data structure configures a remote display deviceto: display the input field to the user; receive at least a firstuser-input datum into the input field; retrieve data describingattributes of the user from a database communicatively connected withthe computing device; and refine the interface query data structurebased on data describing attributes of the user from the database;generating, by the computing device, multiple data multipliers based onthe first user-input datum, wherein: each data multiplier includesmultiple data values including relatively higher data values describingdata indicative of progress of the user toward matching a target; and atleast some data multipliers are generated and scored using a machinelearning model including a classifier configured to correlate the userdatum and the first user-input datum to data describing the target intoan ordered list based on score; generating, by the computing device, astrategy data for the user based on the first user-input datum,relatively higher data values, and the ordered list, wherein: thestrategy data is generated in response to a data multiplier instructiondefined as multiplying at least the first user-input datum by relativelyhigher data values and displaying feedback to the user according to theordered list; and the interface query data structure configures theremote display device to: display at least the strategy data; receive asecond user-input datum through the remote display device correspondingto at least some aspects of the strategy data, the second user-inputdatum demonstrating either a similarity or a dissimilarity to the firstuser-input datum, the strategy data configured to be iterativelyrecalculated based on the second user-input datum by at least themachine learning model; and display the performance data output to theuser.
 15. The method of claim 14, further comprising: retrieving, usingthe computing device, attributes of the user from the database includingcategorical information correlated to a historical range of data. 16.The method of claim 14, further comprising: identifying, using thecomputing device, at least an element of at least some data multipliersbetween a minimum value and a maximum value.
 17. The method of claim 14,further comprising: applying, using the computing device, an informationmomentum multiplier to the user datum and/or the interface query datastructure, wherein the information momentum multiplier is defined by thesecond user-input datum exceeding a pre-defined numerical threshold. 18.The method of claim 14, further comprising: performing, using thecomputing device, data collection from the database, wherein datacollection is defined as gathering and measuring data related to atleast one targeted variable.
 19. The method of claim 14, furthercomprising: displaying, using the remote display device, the input fieldto the user by a Graphical User Interface (GUI) defined as a point ofinteraction between the user and the remote display device.
 20. Themethod of claim 14, further comprising: performing, using the computingdevice, optical character recognition on the first user-input datumand/or the second user-input datum.
 21. The method of claim 14, whereinthe interface query data structure is at least partially based on datadescribing attributes of the user that are retrieved from the databaseincluding categorical information correlated to a historical range ofdata.
 22. The method of claim 14, wherein at least some attributesretrieved from the database describe data relating to motivation of theuser defined as a frequency of completing activities related to thestrategy data.
 23. The method of claim 14, further comprising:displaying, using the remote display device, refinements to theinterface query data structure based on data describing attributes ofthe user from the database as a second input field.
 24. The method ofclaim 14, further comprising: displaying, using the remote displaydevice, the performance data output as a function of a user data changedescriptor generated based on the second user-input datum.